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Author

Date

Permanent Link

Thesis Discipline

Science

Degree Grantor

University of Canterbury

Degree Level

Postgraduate Certificate

Degree Name

Postgraduate Certificate in Antarctic Studies

This paper investigates the applicability of the Artificial Neural Network (ANN) learning
paradigm in deriving sea ice thickness in the Antarctic using a combination of physical
measurements, Landsat-8 satellite imagery, and Synthetic Aperture Radar (SAR)
imagery as training input, validated against physical thickness measurements. The
paper discusses the complexities of sea ice and the difficulties faced in calculating
thickness before detailing the current literature applying ANNs to predicting sea ice
characteristics. Methodology is described in terms of an overview of ANNs and the
optimisations applied in this study, as well as the geospatial processing and data
manipulation undertaken to produce the training and test data sets from sparse point
measurements.
Results indicate that physical field measurements are the principle contributor to model
performance, outperforming the Landsat-8 model and increasing performance in a
combined approach. Error for thickness prediction varied between models: ±14cm
(physical measurements), ±23cm (Landsat-8), and ±21cm (combined Landsat-8 and
physical measurements). The paper concludes with a number of suggested
improvements to the model that future studies should consider as well as calling for
further validation of the model through additional, actual field measurements over
manufactured data points produced through interpolation.